{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: replacing module ALBreIF.\n",
      "WARNING: replacing module BPGF.\n"
     ]
    }
   ],
   "source": [
    "include(\"ALBreIF/ALBreIF.jl\")\n",
    "include(\"BPGF/BPGF.jl\")\n",
    "using .ALBreIF\n",
    "using .BPGF"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0 0.0 … 0.008437018121097484 0.003312343294470063; 0.0 0.0 … 0.0 0.0; … ; 0.010701534834511564 0.0 … 0.0 0.0; 0.0 0.0 … 0.01323274485332715 0.0], [0.03338460599710568 0.013778409502433293 … 0.011371284943054146 0.026071634424064443; 0.01578340664353924 0.02801970562363773 … 0.026194765216632207 0.00718454324519644; … ; 0.024883074960946928 0.02457595182941922 … 0.03041292142502584 0.02790623353575392; 0.011732413006374998 0.023612759590985008 … 0.018332788077764197 0.01843134317258685])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Images\n",
    "ul = \"ORL_Faces/s1/1.pgm\"\n",
    "ig = load(ul)\n",
    "\n",
    "a, b = size(ig)\n",
    "R = 5\n",
    "\n",
    "B = Matrix{Float64}(undef, a*b, 400)\n",
    "for i = 1 : 40\n",
    "    for j = 1 : 10\n",
    "        local url = \"ORL_Faces/s\"*\"$i/\"*\"$j\"*\".pgm\"\n",
    "        local img = load(url)\n",
    "        local q = float64.(channelview(img))\n",
    "        q = q[:]\n",
    "        global B[:, (i-1)*10 + j] = q\n",
    "    end\n",
    "end\n",
    "\n",
    "A = BPGF.normalize!(B)\n",
    "X, Y = BPGF.randinit(A, R^2, 0.2, 1.0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ρ₀ = 0.2 # step size parameter\n",
    "μ₀ = 0.001 # regularization cofficient\n",
    "μ = 0.0 # another regularization coefficient\n",
    "rtime = 300 # runtime\n",
    "version = false"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main.ALBreIF.Result{Float64}([0.0 0.0 … 0.004304030239584686 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.005286552984471398 0.0 … 0.0 0.0; 0.0 0.0 … 0.008920691792542273 0.0], [0.014787814153724247 0.01700807764781408 … 0.015055229768245039 0.013208959361737613; 0.015176781535161615 0.01665799813084358 … 0.013498110961563812 0.011693765626471144; … ; 0.01539083428404899 0.015569339370818008 … 0.015261512813053121 0.013377168282435225; 0.016512067118589543 0.01980446589279188 … 0.015410658249346737 0.013625739052970617], 1645, false, [0.0 0.9999998256595155; 0.24900007247924805 0.9731983031325707; … ; 0.0 0.0; 0.0 0.0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X₀ = copy(X); # OutOfMemoryError\n",
    "# Y₀ = copy(Y);\n",
    "# r₀ = BPGF.solve!(BPGF.BPG{Float64}(runtime=rtime,\n",
    "#                 verbose=version,\n",
    "#                 ρ=0.02,\n",
    "#                 μ₁=μ₀,\n",
    "#                 μ₂=μ), A, X₀, Y₀)\n",
    "\n",
    "\n",
    "X₁ = copy(X);\n",
    "Y₁ = copy(Y);\n",
    "r₁ = BPGF.solve!(BPGF.BBPG{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.2,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₁, Y₁)\n",
    "\n",
    "X₂ = copy(X);\n",
    "Y₂ = copy(Y);\n",
    "r₂ = ALBreIF.solve!(ALBreIF.ABLBreI{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.5,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₂, Y₂)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store experimental results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1645-element Vector{Float64}:\n",
       " 0.9999998256595155\n",
       " 0.9731983031325707\n",
       " 0.9462322922896176\n",
       " 0.9223869677580421\n",
       " 0.8979609214284148\n",
       " 0.873814990410472\n",
       " 0.8500518545876162\n",
       " 0.82743995655056\n",
       " 0.8049400037948349\n",
       " 0.784234557104257\n",
       " ⋮\n",
       " 0.08749051175074415\n",
       " 0.08748868314286255\n",
       " 0.08748661132795399\n",
       " 0.08748566763156619\n",
       " 0.08748504434880498\n",
       " 0.08748372541065685\n",
       " 0.08748179419800399\n",
       " 0.08748132407169203\n",
       " 0.08747106903346298"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stop₀ = r₀.niters\n",
    "# pic₀ = r₀.objvalue\n",
    "# rt₀ = pic₀[1:stop₀, 1]\n",
    "# obj₀ = pic₀[1:stop₀, 2]\n",
    "\n",
    "stop₁ = r₁.niters\n",
    "pic₁ = r₁.objvalue\n",
    "rt₁ = pic₁[1:stop₁, 1]\n",
    "obj₁ = pic₁[1:stop₁, 2]\n",
    "\n",
    "stop₂ = r₂.niters\n",
    "pic₂ = r₂.objvalue\n",
    "rt₂ = pic₂[1:stop₂, 1]\n",
    "obj₂ = pic₂[1:stop₂, 2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using CairoMakie\n",
    "using LaTeXStrings\n",
    "using Colors\n",
    "using AlgebraOfGraphics\n",
    "CairoMakie.activate!()\n",
    "function speed()\n",
    "        # lines(rt₀, obj₀; color=\"#4063D8\", label=\"BPG\", linewidth=3, linestyle=:dashdot,\n",
    "                # )\n",
    "        lines(rt₁, obj₁; color=\"blue\", linewidth=2, linestyle=:solid,\n",
    "                label=\"BBPG\",\n",
    "                figure=(; figure_padding=50, resolution=(1200, 800), font=\"sans\",\n",
    "                        backgroundcolor=:white, fontsize=32),\n",
    "                axis=(; title = L\"\\text{ORL Datasets-Regularization}(\\mu=10^{-3})\",\n",
    "                        xlabel=\"Time(sec)\", ylabel=L\"\\Vert A-XY\\Vert_F\",\n",
    "                        yscale=log10,\n",
    "                        xgridstyle=:dash, ygridstyle=:dash,\n",
    "                        topspinevisible = false, rightspinevisible =false))\n",
    "        lines!(rt₂, obj₂; color=\"#CB3C33\", linewidth=2, linestyle=:solid,\n",
    "                label=\"ABLBreI\")\n",
    "        limits!(0, rtime, 10^(-1.2), 1.0)\n",
    "        axislegend(merge=true)\n",
    "        current_figure()\n",
    "end\n",
    "\n",
    "with_theme(speed, theme=theme_dark())\n",
    "speed()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CairoMakie.Screen{IMAGE}\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save(\"plot/real_data.png\", speed())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "find_closest (generic function with 1 method)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function find_closest(value, arr)\n",
    "    min_diff = abs(arr[1] - value)\n",
    "    closest_index = 1\n",
    "    \n",
    "    for i in 2:length(arr)\n",
    "        diff = abs(arr[i] - value)\n",
    "        if diff < min_diff\n",
    "            min_diff = diff\n",
    "            closest_index = i\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return closest_index\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1933263859275937\n",
      "0.13210070799090687\n",
      "0.10129950265373926\n",
      "0.08748132407169203"
     ]
    }
   ],
   "source": [
    "print(obj₂[find_closest(15, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(30, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(60, rt₂)])\n",
    "print(\"\\n\")\n",
    "print(obj₂[find_closest(300, rt₂)])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.8.3",
   "language": "julia",
   "name": "julia-1.8"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
